Stochastic Process Decision Methods for Complex-Cyber-Physical Systems

Abstract

Research efforts were conducted under this contract to define, quantify, and estimate the complexity of a system. Further, efforts were made to identify major contributors to this estimated system complexity in an effort to inform resource allocation procedures aimed at reducing system complexity. In this research, we have defined complexity as the potential of a system to exhibit unexpected behavior in the quantities of interest. We measure this form of complexity using information entropy. To determine the most significant contributors within a system to this complexity, we derived a sensitivity analysis procedure based on mutual information that rigorously identifies the amount of complexity that could be reduced if complete knowledge of a given system component could be obtained.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2011
Accession Number
ADA552217

Entities

People

  • Chuanlin He
  • D. Allaire
  • G. Sondecker
  • J. Deyst
  • Karen Willcox

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Complex Systems
  • Computational Science
  • Data Science
  • Distribution Functions
  • Gaussian Processes
  • Infantry Fighting Vehicles
  • Information Science
  • Knowledge Management
  • Monte Carlo Method
  • Normal Distribution
  • Probability
  • Random Variables
  • Reliability
  • Statistical Algorithms
  • Stochastic Processes

Fields of Study

  • Computer science
  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Life Cycle Cost Analysis
  • Systems Analysis and Design

Technology Areas

  • Cyber
  • Cyber - Cryptography